Autonomous Guided Vehicles (AGV) have the potential to revolutionize operations in areas such as manufacturing, distribution, transportation, and military. While AGVs can be used to move disabled people around in health-care and nursing facilities, the mundane and often repetitive task of transporting materials in manufacturing and distribution can be efficiently accomplished by using AGVs as well.
In material transport vehicles and systems, AGVs may include fork lift trucks, tuggers, and the like, which are used in a wide variety of applications. The recent successful introduction of robot systems in distribution centers and manufacturing to transport items around have shown the limitations of existing solutions. Disadvantages of current systems include being designed for only one fixed environment and/or addressing only a special case of potential navigation problems.
For instance, the Amazon Kiva robots require streets and highways mapped on the floor with barcode stickers and a central server to coordinate the robots. Even though the robots can detect obstacles such as humans and tipped pods and stop, the grid is intended only for navigation, which makes them less suitable in environments that are reconfigured by moving stations and objects around.
Furthermore, the space requirement for the navigation of hundreds of trucks in a production facility is very high compared to a system that does not require a dedicated track for navigation. Finally, Amazon's system makes sense only for newly acquired AGVs because of the application specific robots used. Users that already operate hundreds of AGVs cannot retrofit them for this solution.
Other approaches deploy robots that use spatial perception by stereoscopic vision and 3D evidence grids. In this approach, robots are first trained to recognized routes and build a map of the facility. Afterwards they can navigate from one point to the next using 3D collision avoidance. For these systems, any reconfiguration that changes a route requires new training which cannot be done in real-time, particularly for very large facilities.
One of the main impediments in current indoor AGV is path building and maintenance. In this regard, most systems rely so far on Simultaneous Localization and Mapping (SLAM), where robots are first trained to recognize routes and then try to build a map of the environment for navigation. In large facilities that are constantly reconfigured, the cost of map building and maintenance becomes too high, thus preventing the feasibility of this approach. SLAM for example is overcome in robots by using an interconnect structure build on the floor. While feasible in new facilities, the cost of retrofitting existing facilities with new robots are also too immense.
Representation of the environment is a prerequisite for coordination and navigation of AGV in an indoor facility. A dynamic 2D map of the facility can be maintained by computer vision algorithms in real-time, provided that whole pictures of the whole facilities are available to the algorithm whenever a change takes place in the environment. Change can be a complete reconfiguration of the facility, a human moving around, or objects lying in the way. Because a single camera cannot provide the entire view of a large facility, distributed cameras represent a straightforward way to collectively cover the complete facility. A distributed set of cameras such as those used in supermarkets can provide a partial view to a central server where the 2D reconstruction of the entire view can be done followed by the construction and dynamic update of the 2D map. The problem with this approach is the bandwidth required to transfer all the pictures to the central server as well as the amount of computing power the server must provide for large facilities. Furthermore, the whole navigation is compromised with potential fatal damages in case of central server failure.
Another system, which is already in use is patented by the Seegrid company. Their vision technology does not require environment modification. Their approach uses SLAM for creating a map of the environment. They are able to navigate from one point to the next one using a 3D collision avoidance. Facility reconfiguration with changes in routes requires a new training, which cannot be done in real-time particularly for very large facilities. The concept published by Reina, et al. has ceiling-mounted, or flying, cameras to monitor a whole area. Despite this, the setup of the robots is that they are not equipped with cameras and therefore do not navigate autonomously. Path planning and robot navigation is centralized at each camera, which tells the robots where to go. In a separate section, the authors use an “occupancy-map”, where the area is divided in cells that can be marked if an object occupies them. Routing is done by using Voronoi-Diagrams for path computation. This method is not fully described, for example the size of the cells is not defined, which can vary from square centimeters to square meters. The number of cells to cover makes the method computational intractable, at least in real-time. Besides the computational burden of this approach, it is as stated earlier, difference in infrastructure and operation: No camera is used on the robot, which are not autonomous. Obstacle avoidance is performed by the cameras. With dozens of robots in a field-of-view (FoV), no camera can provide the computational power to tackle obstacle detection and avoidance and provide guidance to the robots in real-time.
A distributed network of cameras to control robots is also described in Y. Cheng. Wireless mosaic eyes based robot path planning and control. Autonomous robot navigation using environment intelligence with distributed vision sensors. PhD thesis, University of Bradford, 2010. The approach is similar to the previous one where cameras are still in charge of routing and navigation in their FoV. In an extension of this PhD-thesis, the author extends his method to include some low-level tasks to the robots such as low-level perception and control, which the robot needs anyway to move. It was not clear what the authors meant with low-level perception.